We used the ranking numbers over five training iterations for each dataset size as the quality metric describing the stability and reproducibility of the solution. In this case, the splitting technique with the smallest ranking number should represent the most diverse property required to build a model with transfer learning. The best results were obtained with the highest variety of aromatic rings number in the training sets. However, the molecular weight and molecules conformation mobility (number of rotatable bonds) also proved to be important and even more significant for the smallest datasets. Also, it is noteworthy that datasets of 20 molecules were the most stable in relation to any selection criteria. That is, we can assume that 20 molecules with diverse sizes and number of flexible (rotatable bonds) and planar (aromatic ring) parts form a sufficient dataset for transfer learning. The next step to assess the impact of the donor dataset composition on the quality of the acceptor model was to understand the impact of the similarity between these datasets at a constant quantity. For this purpose, the ECFP4 values of the fingerprints were calculated for all molecules of the donor logP and acceptor logS datasets. The obtained vectors transformed into two-dimensional ones using principal components analysis (PCA) [1]} for demonstrative purposes. The results are presented in Fig. 4 (a). The three regions were numbered as the dataset intersection increased, from the area where there was almost no overlap (Area 1) to the area where the compositions of the datasets overlap significantly (Area 3). We extracted 10,000 molecules subsets from each of the regions, as well as a random one for the comparison. Using the obtained subsets, 4 donor models were obtained, which in turn were used to conduct transfer learning. The results are presented in Fig. 4 (b). Despite the presence of a weak correlation of R2 score with the structural similarity of the datasets, we can conclude that the similarity influence was negligible comparing to the influence of donor or acceptor datasets sizes.
